SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels

• URL: http://arxiv.org/abs/2502.03201v1
• Date: Wed, 05 Feb 2025 14:21:03 GMT
• Title: SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels
• Authors: Xiangyu Dong, Xingyi Zhang, Lei Chen, Mingxuan Yuan, Sibo Wang,
• Abstract summary: Node Anomaly Detection (NAD) has gained significant attention in the deep learning community due to its diverse applications in real-world scenarios. Previous methods tend to leverage synthetic data to collect auxiliary information, which is not an effective solution. We introduce a novel SpaceGNN model designed for NAD tasks with extremely limited labels.
• Score: 14.97927835218024
• License:
• Abstract: Node Anomaly Detection (NAD) has gained significant attention in the deep learning community due to its diverse applications in real-world scenarios. Existing NAD methods primarily embed graphs within a single Euclidean space, while overlooking the potential of non-Euclidean spaces. Besides, to address the prevalent issue of limited supervision in real NAD tasks, previous methods tend to leverage synthetic data to collect auxiliary information, which is not an effective solution as shown in our experiments. To overcome these challenges, we introduce a novel SpaceGNN model designed for NAD tasks with extremely limited labels. Specifically, we provide deeper insights into a task-relevant framework by empirically analyzing the benefits of different spaces for node representations, based on which, we design a Learnable Space Projection function that effectively encodes nodes into suitable spaces. Besides, we introduce the concept of weighted homogeneity, which we empirically and theoretically validate as an effective coefficient during information propagation. This concept inspires the design of the Distance Aware Propagation module. Furthermore, we propose the Multiple Space Ensemble module, which extracts comprehensive information for NAD under conditions of extremely limited supervision. Our findings indicate that this module is more beneficial than data augmentation techniques for NAD. Extensive experiments conducted on 9 real datasets confirm the superiority of SpaceGNN, which outperforms the best rival by an average of 8.55% in AUC and 4.31% in F1 scores. Our code is available at https://github.com/xydong127/SpaceGNN.

Related papers

• Into the Void: Mapping the Unseen Gaps in High Dimensional Data [23.226089369715016]
  We present a comprehensive pipeline, augmented by a visual analytics system named GapMiner'' It is aimed at exploring and exploiting untapped opportunities within the empty areas of high-dimensional datasets.
  arXiv  Detail & Related papers  (2025-01-25T16:57:21Z)
• SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection [14.1365182085686]
  We introduce Individual Smoothing Patterns (ISP) and Neighborhood Smoothing Patterns (NSP) ISP and NSP indicate that the representations of anomalous nodes are harder to smooth than those of normal ones. Motivated by these findings, we propose SmoothGNN, a novel unsupervised node anomaly detection framework.
  arXiv  Detail & Related papers  (2024-05-27T14:23:30Z)
• FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution [4.577685231084759]
  This paper proposes a Feature-Enhanced Neural Implicit Representation (FFEINR) for super-resolution of flow field data. It can take full advantage of the implicit neural representation in terms of model structure and sampling resolution. The training process of FFEINR is facilitated by introducing feature enhancements for the input layer.
  arXiv  Detail & Related papers  (2023-08-24T02:28:18Z)
• Efficient GNN Explanation via Learning Removal-based Attribution [56.18049062940675]
  We propose a framework of GNN explanation named LeArn Removal-based Attribution (LARA) to address this problem. The explainer in LARA learns to generate removal-based attribution which enables providing explanations with high fidelity. In particular, LARA is 3.5 times faster and achieves higher fidelity than the state-of-the-art method on the large dataset ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-06-09T08:54:20Z)
• Combating Mode Collapse in GANs via Manifold Entropy Estimation [70.06639443446545]
  Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications. We propose a novel training pipeline to address the mode collapse issue of GANs.
  arXiv  Detail & Related papers  (2022-08-25T12:33:31Z)
• EIGNN: Efficient Infinite-Depth Graph Neural Networks [51.97361378423152]
  Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. Motivated by this limitation, we propose a GNN model with infinite depth, which we call Efficient Infinite-Depth Graph Neural Networks (EIGNN) We show that EIGNN has a better ability to capture long-range dependencies than recent baselines, and consistently achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-02-22T08:16:58Z)
• GAN Based Boundary Aware Classifier for Detecting Out-of-distribution Samples [24.572516991009323]
  We propose a GAN based boundary aware classifier (GBAC) for generating a closed hyperspace which only contains most id data. Our method is based on the fact that the traditional neural net seperates the feature space as several unclosed regions which are not suitable for ood detection. With GBAC as an auxiliary module, the ood data distributed outside the closed hyperspace will be assigned with much lower score, allowing more effective ood detection.
  arXiv  Detail & Related papers  (2021-12-22T03:35:54Z)
• Exploring the Common Principal Subspace of Deep Features in Neural Networks [50.37178960258464]
  We find that different Deep Neural Networks (DNNs) trained with the same dataset share a common principal subspace in latent spaces. Specifically, we design a new metric $mathcalP$-vector to represent the principal subspace of deep features learned in a DNN. Small angles (with cosine close to $1.0$) have been found in the comparisons between any two DNNs trained with different algorithms/architectures.
  arXiv  Detail & Related papers  (2021-10-06T15:48:32Z)
• Pure Exploration in Kernel and Neural Bandits [90.23165420559664]
  We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we propose to adaptively embed the feature representation of each arm into a lower-dimensional space.
  arXiv  Detail & Related papers  (2021-06-22T19:51:59Z)
• EXoN: EXplainable encoder Network [0.0]
  We propose a new semi-supervised learning method of Variational AutoEncoder (VAE) which yields explainable latent space by EXplainable encoder Network (EXoN) Negative cross-entropy and Kullback-Leibler divergence play a crucial role in constructing explainable latent space.
  arXiv  Detail & Related papers  (2021-05-23T07:04:30Z)
• Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction [48.73525876467408]
  We propose a novel technique for hyperspectral subspace analysis. The technique is called joint and progressive subspace analysis (JPSA) Experiments are conducted to demonstrate the superiority and effectiveness of the proposed JPSA on two widely-used hyperspectral datasets.
  arXiv  Detail & Related papers  (2020-09-21T16:29:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.